AWS announced the general availability of Lambda SnapStart for Python and .NET functions. This feature boosts functions' startup times to sub-second levels, often with minimal code changes, enabling highly responsive and scalable serverless apps. Lambda SnapStart works by caching and reusing the snapshotted memory and disk state of any one-time initialization code, or code that runs only the first time a Lambda function is invoked. Lambda takes a Firecracker microVM snapshot of the memory and disk state of the initialized execution environment, encrypts the snapshot, and caches it for low-latency access. When you invoke the function version for the first time, and as the invocations scale up, Lambda resumes new execution environments from the cached snapshot instead of initializing them from scratch, improving startup latency. For Python functions, startup latency from initialization code can be several seconds long. Some scenarios where this can occur are loading dependencies (such as LangChain, Numpy, Pandas, and DuckDB) or using frameworks (such as Flask or Django). For .NET functions, we expect most use cases to benefit because .NET just-in-time (JIT) compilation takes up to several seconds. Latency variability associated with initialization of Lambda functions has been a long-standing barrier for customers to use .NET for AWS Lambda. SnapStart enables functions to resume quickly by caching a snapshot of their memory and disk state. Therefore, most .NET functions will experience significant improvement in latency variability with Lambda SnapStart.